Field of the Invention
The present invention relates to a reconstruction method of a biological tissue image and an apparatus therefor, and particularly relates to a method for reconstructing a biological tissue image from measured spectrum data which is correlated with a substance distributed within a biological tissue, and to an apparatus therefor. The present invention also relates to an image display apparatus for clearly displaying a lesion at a pathological diagnosis by using thus acquired biological tissue image.
Description of the Related Art
Conventionally, a pathological diagnosis has been conducted which is specifically a diagnosis for the presence or absence of a lesion and a type of the lesion, based on the observation for a biological tissue of an object by a microscope or the like. In the pathological diagnosis, a constitutive substance and a contained substance which are correlated with a biological tissue of an object to be observed are required to be visualized. So far, a technique for staining a specific antigen protein by using an immunostaining method has mainly been employed in the pathological diagnosis. When breast cancer is taken as an example, ER (estrogen receptor which is expressed in hormone-dependent tumor) which serves as a determination criterion for hormone therapy and HER2 (membrane protein to be found in fast-growing cancer) which serves as a determination criterion for Herceptin administration are visualized by the immunostaining method. However, the immunostaining method has such problems that the reproducibility is poor because an antibody is unstable and antigen-antibody reaction efficiency is difficult to be controlled. In addition, when needs of such a functional diagnosis will be grown in the future, for instance, and when there arises a need of detecting several tens or more types of constitutive substances or contained substances, the currently-employed immunostaining method has a problem of being incapable of meeting the need any more.
In addition, in some cases, the visualization of the substance which is distributed within a biological tissue, such as the constitutive substance and the contained substance, is not sufficient at a tissue level, and the visualization at a cellular level is required. For instance, in research on a cancer stem cell, it was revealed that a tumor was formed in only part of fractions of a tumor tissue after xenotransplantation to immunocompromised mice, and accordingly, it is being understood that the growth of a tumor tissue is dependent on differentiation and self-reproduction abilities of the cancer stem cells. In such examination, it is necessary not to observe the entire tissue, but to observe an expression distribution of a constitutive substance or a contained substance in each of individual cells in a tissue.
Incidentally, the above described “cellular level” means a level at which at least each of the individual cells can be classified. A diameter of the cell exists in a range of approximately 10 μm to 20 μm (provided that large cell such as nerve cell has diameter of about 50 μm). Accordingly, in order to acquire a two-dimensional distribution image at a cellular level, the spatial resolution needs to be 10 μm or less, can be 5 μm or less, further can be 2 μm or less, and still further can be 1 μm or less. The spatial resolution can be determined from a result of, for instance, a linear analysis of a knife-edge specimen. In other words, the spatial resolution is determined based on the general definition of “a distance between two points at which signal intensities originating in a concerned substance in the vicinity of the boundary of a specimen are 20% and 80%, respectively.”
As described above, in the pathological diagnosis, the constitutive substance and the contained substance which are correlated with a lesion or a pathological tissue are required to be exhaustively visualized at a cellular level. The lesion or the pathological tissue means, for instance, a tumor tissue and the like. Candidates for a method of such visualization include secondary-ion mass spectrometry (SIMS) including time-of-flight secondary-ion mass spectrometry (TOF-SIMS). A mass spectrum is used as a measured spectrum. Furthermore, the candidates include also Raman spectroscopy. Usable measured spectra include spectra in an ultraviolet region, a visible region and an infrared region. These measurement methods can provide information at each of plural points in a space at high spatial resolution. Specifically, the measurement methods can provide spatial distribution information on each peak value of the measured spectrum which is correlated with a substance that is an object to be measured, and accordingly, can determine a spatial distribution of the substance in a biological tissue which is correlated with the measured spectrum.
An SIMS method is a method of obtaining a mass spectrum at each point on a specimen by irradiating the specimen with a primary ion beam and detecting secondary ions which have been separated from the specimen. In a TOF-SIMS, for instance, it is possible to obtain the mass spectrum at each point on the specimen by identifying the secondary ion with the use of such a fact that a flight time of the secondary ion depends on a mass m and an electric charge z of the ion.
A Raman spectroscopy acquires a Raman spectrum by irradiating a substance with a laser beam which is a monochromatic light as a light source, and detecting the generated Raman scattering light with a spectroscope or an interferometer. A difference (Raman shift) between a frequency of the Raman scattering light and a frequency of incident light takes a value peculiar to the structure of the substance, and accordingly the Raman spectroscopy can acquire the Raman spectrum peculiar to an object to be measured.
In order to acquire biological information from data of the measured spectrum, a conventional method has generated a classifier beforehand by machine learning, and has applied the generated classifier to the data of the measured spectrum of the specimen (see Japanese Patent Application Laid-Open No. 2010-71953). On the other hand, it has been attempted to overlap a measured spectrum image (spectrum information) with an optical image (morphological information) and display the overlapped image, because a biological tissue image is indispensable in a pathological diagnosis (see Japanese Patent Application Laid-Open No. 2010-85219). Incidentally, the machine learning described here means a technique of empirically learning data which have been previously acquired, and interpreting newly acquired data based on the learning results. Further, the classifier refers to determination criterion information to be generated by empirically learning a relationship between previously acquired data and biological information.
Conventionally, an example of diagnosing a disease by applying the classifier which has been generated by the machine learning is described also in Patent Document 1. The object to be diagnosed is one measured spectrum data (for one point on space or whole specimen), and it has not been assumed to acquire the biological tissue image from a spatial distribution of the measured spectrum. In addition, there is an example of overlapping the measured spectrum image (spectrum information) with the optical image (morphological information), but there has been no example of acquiring the biological tissue image by applying the machine learning (classifier) to both the spectrum information and the morphological information. Specifically, such a method has not been disclosed as to reconstruct a biological tissue image with high precision, which displays a diagnosis result related to a presence or an absence of a cancer and the like, from a result of having measured a spectrum having the spatial distribution for the biological tissue of an object.
In addition, when the measured spectrum has the spatial distribution, the characteristics of the data are different between positions at which the data is measured, for instance, a datum measured in the middle part of the image is different in the characteristics from that measured in the peripheral portion of the image. Accordingly, the classifier suitable for the position needs to be applied according to the position at which the data is measured. However, conventionally, such a method has not been disclosed as to have assumed such a situation.